Abstract
This paper presents a robust adaptive repetitive control (RARC) method for a class of periodically time-varying nonlinear systems with aperiodic uncertainties. A σ modification is introduced in the learning algorithm of RARC, in order to guarantee robustness of the system undertaken. The closed-loop type learning algorithm is examined and it is shown that the realisability cannot be assured when the σ modification is applied. To avoid the causality contradiction, an open-loop type learning algorithm with switching σ modification is proposed to guarantee robustness and achieve the asymptotic convergence of the tracking error, when the disturbances disappear. Extension to the RARC for robotic manipulators is given and the numerical simulation is carried out to verify the effectiveness of the learning control scheme.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
